BETO Spanish Clickbaits Model
This clickbait analysis model is based on the BETO, a Spanish variant of BERT.
Model Details
BETO is a BERT model trained on a big Spanish corpus. BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique.
Model fine-tuned with a news (around ~30k) of several Spanish Newspapers.
Training evaluate
Using transformers
BATCH_SIZE = 100
NUM_PROCS = 32
LR = 0.00005
EPOCHS = 5
MAX_LENGTH = 25
MODEL = 'dccuchile/bert-base-spanish-wwm-cased'
{'eval_loss': 0.0386480949819088,
'eval_accuracy': 0.9872786230980294,
'eval_runtime': 10.0476,
'eval_samples_per_second': 398.999,
'eval_steps_per_second': 4.081,
'epoch': 5.0}
Uses
This model is designed to classify newspaper news as clickbaits or not.
You can see a use case in this url: Spanish Newspapers
Direct Use
from transformers import (
AutoTokenizer,
AutoModelForSequenceClassification,
TextClassificationPipeline,
)
tokenizer = AutoTokenizer.from_pretrained("taniwasl/clickbait_es")
model = AutoModelForSequenceClassification.from_pretrained("taniwasl/clickbait_es")
review_text = 'La explosión destruye parcialmente el edificio, Egipto'
nlp = TextClassificationPipeline(task = "text-classification",
model = model,
tokenizer = tokenizer,
max_length = 25,
truncation=True,
add_special_tokens=True
)
print(nlp(review_text))
License Disclaimer
The license MIT best describes our intentions for our work. However we are not sure that all the datasets used to train BETO have licenses compatible with MIT (specially for commercial use). Please use at your own discretion only for no commercial use.
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